6476 papers
This paper introduces Geodesic Gradient Descent (GGD), a generic, learning-rate-free optimization algorithm that approximates local neighborhoods of objective function-induced hypersurfaces using n-dimensional spheres to ensure update trajectories remain on the manifold, achieving significant performance improvements over Adam on both regression and classification tasks.
This study demonstrates that the choice of graph construction technique significantly impacts IoT botnet detection performance, revealing that Gabriel graphs combined with Variational Autoencoders and Graph Attention Networks achieve the highest accuracy (97.56%) on the N-BaIoT dataset compared to other methods like k-NN and Shared Nearest Neighbor.
This paper introduces Projection-Augmented Graph (PAG), a novel Approximate Nearest Neighbor Search framework that integrates projection techniques into graph indexing to simultaneously achieve high query efficiency, fast indexing, low memory usage, and robust scalability across modern AI workloads, outperforming existing methods like HNSW by up to 5x in speed while supporting online insertions.
The paper introduces EnsAug, a novel training paradigm that improves human motion analysis by replacing the conventional single-model approach with an ensemble of specialists, each trained on data augmented by a distinct geometric transformation, thereby achieving state-of-the-art performance while respecting kinematic constraints.
The paper introduces HyperTokens, a transformer-based token generator augmented with meta-inspired regularizers and causal auxiliary supervision to enable efficient, low-forgetting continual Video-Language Understanding by dynamically producing task-specific prompts without increasing memory costs.
This paper introduces ERP-RiskBench, a leakage-safe ensemble learning framework for detecting financial risks in ERP systems that combines hybrid risk definitions, rigorous time- and group-aware validation to prevent data leakage, and a stacking gradient boosting model to deliver reproducible, interpretable, and operationally grounded fraud detection results.
This paper proposes an unsupervised CNN autoencoder with a novel weighted spectral angle distance loss to enable blind, automated unmixing of complex ATR-FTIR hyperspectral images from historical oil painting cross-sections, significantly improving the interpretability and scalability of material analysis compared to traditional manual methods.
This paper demonstrates that Graph Neural Networks outperform traditional models like TabNet in estimating muon particle momentum for the CMS experiment, highlighting the critical role of node feature dimensionality and the benefits of leveraging the data's inherent graph structure to improve trigger system efficiency.
This paper proposes a hybrid few-shot learning model integrating Siamese and Prototypical Networks with Grad-CAM-based Explainable AI to achieve high-accuracy, interpretable classification of maize, rice, and wheat leaf diseases under limited data conditions.
This paper addresses the limitations of current Large Vision-Language Models in deep chart research by proposing Parallel Relative Policy Optimization (PRPO) to resolve training conflicts and constructing the MCDR-Bench evaluation framework to enable objective assessment of complex reasoning capabilities.
This paper proposes a plug-and-play method called ABMS, which combines an additional backward denoising step with Monte-Carlo sampling to reduce estimation errors in posterior sampling and thereby improve the quality and consistency of training-free, loss-guided conditional generation across diverse tasks.
This paper presents an unsupervised learning framework utilizing convolutional filtering and neural networks with optimized early-stopping to achieve robust, high-fidelity reconstruction of ion-beam emittance images from noisy data, enabling unprecedented halo resolution beyond seven standard deviations for particle accelerator diagnostics.
This paper proposes Soft Equivariance Regularization (SER), a lightweight, plug-in method that decouples invariance and equivariance objectives by enforcing equivariance on intermediate spatial features while preserving invariance on the final embedding, thereby improving both linear evaluation accuracy and robustness to geometric perturbations without requiring auxiliary heads or transformation labels.
This paper argues that RGB-only Multimodal Large Language Models fail to generalize across different cameras due to entangled perspective and object properties, and proposes a Camera-Aware MLLM framework that integrates camera intrinsics, augmented data, and 3D geometric priors to achieve robust, generalizable spatial intelligence.
This paper enhances the reliability of solar flare regression by applying conformal prediction to deep learning models, demonstrating that conformalized quantile regression outperforms alternative methods in achieving valid coverage rates and favorable interval lengths for space weather forecasting.
The paper introduces ATLAS, a reinforcement finetuning framework that enables small language models to effectively navigate large toolspaces by learning adaptive context acquisition and execution strategies, thereby achieving frontier-level performance with significantly reduced parameter and context budgets.
This paper presents an integrated pipeline combining knowledge-grounded generative modeling with automated LLM-based auditing to produce clinically consistent, privacy-preserving synthetic patient trajectories that overcome the limitations of existing methods by eliminating clinical inconsistencies while maintaining high statistical fidelity and downstream utility.
The paper introduces ProtAlign, a contrastive learning framework that unifies protein sequence and structure representations into a shared embedding space, thereby enabling cross-modal retrieval and improving downstream tasks like function annotation and stability estimation.
This paper proposes a bi-directional feedback fusion framework that integrates human activity embeddings with dual-timescale temporal modules to significantly improve the accuracy and interpretability of indoor CO2 and PM2.5 forecasting compared to traditional data-driven models.
This paper introduces FutureBoosting, a hybrid-AI framework that enhances electricity price forecasting by integrating forecasted features from a frozen time series foundation model into a regression model, thereby achieving significant accuracy improvements over state-of-the-art baselines while maintaining interpretability.